The urban WWTP not only guarantees the reliability and stability of the wastewater treatment system but also meets the water quality national discharge standard. However, the influence factors are various for effluent TN concentration of wastewater treatment process and the relationship between different influencing factors are complex. Therefore, it is hard to make real-time detecting for effluent TN concentration, which seriously affected the stable operation of the urban WWTP. The computing implemented method for effluent TN concentration, based on RSORBFNN, is helpful to improve the efficiency, strengthen delicacy management and ensure water quality effluent standards of urban WWTP. It has better economic benefit as well as significant environmental and social benefits. Thus, the research achievements have wide application prospect in this present disclosure.
The control target of urban WWTP is to make the water quality meet the national discharge standards, mainly related to the parameters of effluent TN concentration, chemical oxygen demand (COD), effluent suspended solids (SS), ammonia nitrogen (NH4-N), biochemical oxygen demand (BOD) and effluent total phosphorus (TP). Effluent TN concentration refers to the sum of all the nitrogen pollution of the water after dealing with the sewage treatment plant process facilities, mainly for the ammonia nitrogen, nitrate nitrogen, inorganic nitrogen, protein, amino acid and organic amine organic nitrogen combined. According to statistics, nitrogen fixation rate of about 150 million tons per year in nature and chemical nitrogen fertilizer production rate of about 5000˜6000 tons a year. If nature denitrification reaction failed to complete the nitrogen cycle, too much nitrogen compounds and the ammonia nitrogen nutrient caused a significant number of algae in the water, the plants breeding, appearance of eutrophication status. To curb the trend of worsening of water environment, many sewage treatment facilities have spent a large sum of money to build and put into operation in the country, the cities, and towns. The general method for determination is the alkaline potassium persulfate UV spectrophotometry and molecular absorption spectrometry. However, the determination of total nitrogen TN is often offline and can't realize the effluent TN concentration real-time measurement, which led directly to the sewage treatment process is hard to achieve closed loop control. Moreover, it is a big challenge for detection due to a significant amount of pollutants in wastewater and different content. Developing new hardware measuring instrument, although directly solving various wastewater treatment process variables and the detection problem of water quality parameters, due to the very complex organic matter in sewage, research and development of the new sensor will be a significant cost and a time-consuming project. Hence, the new method presented to solve the problem of the real-time measurement of the process parameters of WWTP has become an important topic to research in the field of wastewater control engineering and has important practical significance.
To obtain more reliable information on effluent TN concentration in urban WWTP, we have investigated a computing implemented method based on the RSORBFNN. The neural network uses competitiveness of the hidden neuron to determine whether to add or delete the hidden neurons and to use an adaptive second order algorithm to ensure the accuracy of RSORBFNN. The objective of this present disclosure is to develop a computing implemented method for estimating the effluent TN concentration online and with high precision.